Gridded Datasets II

import numpy as np
import xarray as xr
import holoviews as hv
import geoviews as gv
import geoviews.feature as gf

from geoviews import opts
from cartopy import crs as ccrs

gv.extension('matplotlib', 'bokeh')

gv.output(size=200)

Alert: Some widgets in this notebook are set to render dynamically and require a live notebook server. If you're viewing this notebook on a static website, the widgets won't respond.

The main strength of HoloViews and its extensions (like GeoViews) is the ability to quickly explore complex datasets by declaring lower-dimensional views into a higher-dimensional space. In HoloViews we refer to the interface that allows you to do this as the conversion API. To begin with we will load a multi-dimensional dataset of surface temperatures for different “realizations” (modelling parameters) using XArray:

xr_ensembles = xr.open_dataset('../data/ensembles.nc')
xr_ensembles

<xarray.Dataset>
Dimensions:                  (realization: 13, time: 6, latitude: 145,
                              longitude: 192, bnds: 2)
Coordinates:
  * realization              (realization) int32 0 1 2 3 4 5 7 8 9 10 11 12 13
  * time                     (time) datetime64[ns] 2011-08-16T12:00:00 ... 20...
  * latitude                 (latitude) float32 -90.0 -88.75 ... 88.75 90.0
  * longitude                (longitude) float32 0.0 1.875 3.75 ... 356.2 358.1
    forecast_reference_time  (realization, time) datetime64[ns] ...
Dimensions without coordinates: bnds
Data variables:
    surface_temperature      (realization, time, latitude, longitude) float32 ...
    latitude_longitude       int32 ...
    time_bnds                (time, bnds) datetime64[ns] ...
Attributes:
    source:       Data from Met Office Unified Model
    um_version:   7.6
    Conventions:  CF-1.5

xarray.Dataset

• Dimensions:
  • realization: 13
  • time: 6
  • latitude: 145
  • longitude: 192
  • bnds: 2
• Coordinates: (5)
  • realization
    (realization)
    int32
    0 1 2 3 4 5 7 8 9 10 11 12 13

    units :

    1

    standard_name :

    realization

    array([ 0,  1,  2,  3,  4,  5,  7,  8,  9, 10, 11, 12, 13], dtype=int32)

  • time
    (time)
    datetime64[ns]
    2011-08-16T12:00:00 ... 2012-01-...

    axis :

    T

    bounds :

    time_bnds

    standard_name :

    time

    array(['2011-08-16T12:00:00.000000000', '2011-09-16T00:00:00.000000000',
           '2011-10-16T12:00:00.000000000', '2011-11-16T00:00:00.000000000',
           '2011-12-16T12:00:00.000000000', '2012-01-16T12:00:00.000000000'],
          dtype='datetime64[ns]')

  • latitude
    (latitude)
    float32
    -90.0 -88.75 -87.5 ... 88.75 90.0

    axis :

    Y

    units :

    degrees_north

    standard_name :

    latitude

    array([-90.  , -88.75, -87.5 , -86.25, -85.  , -83.75, -82.5 , -81.25, -80.  ,
           -78.75, -77.5 , -76.25, -75.  , -73.75, -72.5 , -71.25, -70.  , -68.75,
           -67.5 , -66.25, -65.  , -63.75, -62.5 , -61.25, -60.  , -58.75, -57.5 ,
           -56.25, -55.  , -53.75, -52.5 , -51.25, -50.  , -48.75, -47.5 , -46.25,
           -45.  , -43.75, -42.5 , -41.25, -40.  , -38.75, -37.5 , -36.25, -35.  ,
           -33.75, -32.5 , -31.25, -30.  , -28.75, -27.5 , -26.25, -25.  , -23.75,
           -22.5 , -21.25, -20.  , -18.75, -17.5 , -16.25, -15.  , -13.75, -12.5 ,
           -11.25, -10.  ,  -8.75,  -7.5 ,  -6.25,  -5.  ,  -3.75,  -2.5 ,  -1.25,
             0.  ,   1.25,   2.5 ,   3.75,   5.  ,   6.25,   7.5 ,   8.75,  10.  ,
            11.25,  12.5 ,  13.75,  15.  ,  16.25,  17.5 ,  18.75,  20.  ,  21.25,
            22.5 ,  23.75,  25.  ,  26.25,  27.5 ,  28.75,  30.  ,  31.25,  32.5 ,
            33.75,  35.  ,  36.25,  37.5 ,  38.75,  40.  ,  41.25,  42.5 ,  43.75,
            45.  ,  46.25,  47.5 ,  48.75,  50.  ,  51.25,  52.5 ,  53.75,  55.  ,
            56.25,  57.5 ,  58.75,  60.  ,  61.25,  62.5 ,  63.75,  65.  ,  66.25,
            67.5 ,  68.75,  70.  ,  71.25,  72.5 ,  73.75,  75.  ,  76.25,  77.5 ,
            78.75,  80.  ,  81.25,  82.5 ,  83.75,  85.  ,  86.25,  87.5 ,  88.75,
            90.  ], dtype=float32)

  • longitude
    (longitude)
    float32
    0.0 1.875 3.75 ... 356.2 358.1

    axis :

    X

    units :

    degrees_east

    standard_name :

    longitude

    array([  0.   ,   1.875,   3.75 ,   5.625,   7.5  ,   9.375,  11.25 ,  13.125,
            15.   ,  16.875,  18.75 ,  20.625,  22.5  ,  24.375,  26.25 ,  28.125,
            30.   ,  31.875,  33.75 ,  35.625,  37.5  ,  39.375,  41.25 ,  43.125,
            45.   ,  46.875,  48.75 ,  50.625,  52.5  ,  54.375,  56.25 ,  58.125,
            60.   ,  61.875,  63.75 ,  65.625,  67.5  ,  69.375,  71.25 ,  73.125,
            75.   ,  76.875,  78.75 ,  80.625,  82.5  ,  84.375,  86.25 ,  88.125,
            90.   ,  91.875,  93.75 ,  95.625,  97.5  ,  99.375, 101.25 , 103.125,
           105.   , 106.875, 108.75 , 110.625, 112.5  , 114.375, 116.25 , 118.125,
           120.   , 121.875, 123.75 , 125.625, 127.5  , 129.375, 131.25 , 133.125,
           135.   , 136.875, 138.75 , 140.625, 142.5  , 144.375, 146.25 , 148.125,
           150.   , 151.875, 153.75 , 155.625, 157.5  , 159.375, 161.25 , 163.125,
           165.   , 166.875, 168.75 , 170.625, 172.5  , 174.375, 176.25 , 178.125,
           180.   , 181.875, 183.75 , 185.625, 187.5  , 189.375, 191.25 , 193.125,
           195.   , 196.875, 198.75 , 200.625, 202.5  , 204.375, 206.25 , 208.125,
           210.   , 211.875, 213.75 , 215.625, 217.5  , 219.375, 221.25 , 223.125,
           225.   , 226.875, 228.75 , 230.625, 232.5  , 234.375, 236.25 , 238.125,
           240.   , 241.875, 243.75 , 245.625, 247.5  , 249.375, 251.25 , 253.125,
           255.   , 256.875, 258.75 , 260.625, 262.5  , 264.375, 266.25 , 268.125,
           270.   , 271.875, 273.75 , 275.625, 277.5  , 279.375, 281.25 , 283.125,
           285.   , 286.875, 288.75 , 290.625, 292.5  , 294.375, 296.25 , 298.125,
           300.   , 301.875, 303.75 , 305.625, 307.5  , 309.375, 311.25 , 313.125,
           315.   , 316.875, 318.75 , 320.625, 322.5  , 324.375, 326.25 , 328.125,
           330.   , 331.875, 333.75 , 335.625, 337.5  , 339.375, 341.25 , 343.125,
           345.   , 346.875, 348.75 , 350.625, 352.5  , 354.375, 356.25 , 358.125],
          dtype=float32)

  • forecast_reference_time
    (realization, time)
    datetime64[ns]
    ...

    standard_name :

    forecast_reference_time

    [78 values with dtype=datetime64[ns]]

• Data variables: (3)
  • surface_temperature
    (realization, time, latitude, longitude)
    float32
    ...

    standard_name :

    surface_temperature

    units :

    K

    um_stash_source :

    m01s00i024

    cell_methods :

    time: mean (interval: 1 hour)

    grid_mapping :

    latitude_longitude

    [2171520 values with dtype=float32]

  • latitude_longitude
    ()
    int32
    ...

    grid_mapping_name :

    latitude_longitude

    longitude_of_prime_meridian :

    0.0

    earth_radius :

    6371229.0

    [1 values with dtype=int32]

  • time_bnds
    (time, bnds)
    datetime64[ns]
    ...

    [12 values with dtype=datetime64[ns]]

• Indexes: (4)
  • realization
    PandasIndex

    PandasIndex(Index([0, 1, 2, 3, 4, 5, 7, 8, 9, 10, 11, 12, 13], dtype='int32', name='realization'))

  • time
    PandasIndex

    PandasIndex(DatetimeIndex(['2011-08-16 12:00:00', '2011-09-16 00:00:00',
                   '2011-10-16 12:00:00', '2011-11-16 00:00:00',
                   '2011-12-16 12:00:00', '2012-01-16 12:00:00'],
                  dtype='datetime64[ns]', name='time', freq=None))

  • latitude
    PandasIndex

    PandasIndex(Index([ -90.0, -88.75,  -87.5, -86.25,  -85.0, -83.75,  -82.5, -81.25,  -80.0,
           -78.75,
           ...
            78.75,   80.0,  81.25,   82.5,  83.75,   85.0,  86.25,   87.5,  88.75,
             90.0],
          dtype='float32', name='latitude', length=145))

  • longitude
    PandasIndex

    PandasIndex(Index([    0.0,   1.875,    3.75,   5.625,     7.5,   9.375,   11.25,  13.125,
              15.0,  16.875,
           ...
            341.25, 343.125,   345.0, 346.875,  348.75, 350.625,   352.5, 354.375,
            356.25, 358.125],
          dtype='float32', name='longitude', length=192))

• Attributes: (3)

  source :

  Data from Met Office Unified Model

  um_version :

  7.6

  Conventions :

  CF-1.5

As we saw in the Gridded Datasets I Tutorial we can easily wrap this xarray data structure as a HoloViews Dataset:

dataset = gv.Dataset(xr_ensembles, vdims='surface_temperature')
dataset

:Dataset   [realization,time,latitude,longitude]   (surface_temperature)

From the repr we can immediately see the list of key dimensions (time, realization, longitude and latitude) and the value dimension of the cube (surface_temperature). However, unlike most other HoloViews Elements, the Dataset Element does not display itself visually. This is because it can be n-dimensional and therefore does not have any specific straightforward visual representation on a 2D display. To view the cube, we first have to transform it into individually visualizable chunks. Before doing so, we will want to supply a custom value formatter for the time dimension so that it is readable by humans:

hv.Dimension.type_formatters[np.datetime64] = '%Y-%m-%d'

Conversions

A HoloViews Dataset is a wrapper around a complex multi-dimensional datastructure which allows the user to convert their data into individually visualizable views, each usually of lower dimensionality. This is done by grouping the data by some dimension and then casting it to a specific Element type, which visualizes itself.

The dataset.to interface makes this especially easy. To use it, you supply the Element type that you want to view the data as and the key dimensions of that view and it will figure out the rest. Depending on the type of Element, you can specify one or more dimensions to be displayed. GeoViews provides a set of GeoElements that allow you to display geographic data on a cartographic projection, but you can use any Elements from HoloViews for non-geographic plots.

Recall that the cube we are working with has 4 coordinate dimensions (or key dimensions as they are known in HoloViews) – time, realization, longitude, and latitude. For our purposes, a geographic plot is defined as a plot that has longitude along the x axis and latitude along the y axis. To declare a two-dimensional geographic plot, we therefore simply request a gv.Image plot with longitude and latitude as key dimensions. There is one value dimension (vdim) available, surface_temperature, and any remaining key dimensions (time and realization in this case) are assigned to a HoloMap data structure by default. The resulting HoloMap gives you widgets automatically to allow you to explore the data across the two “remaining” key dimensions (those not mapped onto axes of the image):

geo_dims = ['longitude', 'latitude']
(dataset.to(gv.Image, geo_dims) * gf.coastline)[::5, ::5]

In this way we can visualize the geographic data in a number of ways, currently either as an Image (as above) or as LineContours, FilledContours, or Points:

layout = hv.Layout([dataset.to(el, geo_dims)[::10, ::5] * gf.coastline
                    for el in [gv.FilledContours, gv.LineContours, gv.Points]]).cols(1)

layout.opts(opts.Points(color='surface_temperature', cmap='jet'))

Note that by default the conversion interface will automatically expand all the individual Elements, which can take some time if the data is very large. Instead we can also request the objects to be expanded dynamically using the dynamic keyword:

dataset.to(gv.Image, geo_dims, dynamic=True) * gf.coastline

---------------------------------------------------------------------------
AttributeError                            Traceback (most recent call last)
File /usr/share/miniconda3/envs/test-environment/lib/python3.9/site-packages/IPython/core/formatters.py:974, in MimeBundleFormatter.__call__(self, obj, include, exclude)
    971     method = get_real_method(obj, self.print_method)
    973     if method is not None:
--> 974         return method(include=include, exclude=exclude)
    975     return None
    976 else:

File /usr/share/miniconda3/envs/test-environment/lib/python3.9/site-packages/holoviews/core/dimension.py:1286, in Dimensioned._repr_mimebundle_(self, include, exclude)
   1279 def _repr_mimebundle_(self, include=None, exclude=None):
   1280     """
   1281     Resolves the class hierarchy for the class rendering the
   1282     object using any display hooks registered on Store.display
   1283     hooks.  The output of all registered display_hooks is then
   1284     combined and returned.
   1285     """
-> 1286     return Store.render(self)

File /usr/share/miniconda3/envs/test-environment/lib/python3.9/site-packages/holoviews/core/options.py:1428, in Store.render(cls, obj)
   1426 data, metadata = {}, {}
   1427 for hook in hooks:
-> 1428     ret = hook(obj)
   1429     if ret is None:
   1430         continue

File /usr/share/miniconda3/envs/test-environment/lib/python3.9/site-packages/holoviews/ipython/display_hooks.py:287, in pprint_display(obj)
    285 if not ip.display_formatter.formatters['text/plain'].pprint:
    286     return None
--> 287 return display(obj, raw_output=True)

File /usr/share/miniconda3/envs/test-environment/lib/python3.9/site-packages/holoviews/ipython/display_hooks.py:261, in display(obj, raw_output, **kwargs)
    259 elif isinstance(obj, (HoloMap, DynamicMap)):
    260     with option_state(obj):
--> 261         output = map_display(obj)
    262 elif isinstance(obj, Plot):
    263     output = render(obj)

File /usr/share/miniconda3/envs/test-environment/lib/python3.9/site-packages/holoviews/ipython/display_hooks.py:149, in display_hook.<locals>.wrapped(element)
    147 try:
    148     max_frames = OutputSettings.options['max_frames']
--> 149     mimebundle = fn(element, max_frames=max_frames)
    150     if mimebundle is None:
    151         return {}, {}

File /usr/share/miniconda3/envs/test-environment/lib/python3.9/site-packages/holoviews/ipython/display_hooks.py:209, in map_display(vmap, max_frames)
    206     max_frame_warning(max_frames)
    207     return None
--> 209 return render(vmap)

File /usr/share/miniconda3/envs/test-environment/lib/python3.9/site-packages/holoviews/ipython/display_hooks.py:76, in render(obj, **kwargs)
     73 if renderer.fig == 'pdf':
     74     renderer = renderer.instance(fig='png')
---> 76 return renderer.components(obj, **kwargs)

File /usr/share/miniconda3/envs/test-environment/lib/python3.9/site-packages/holoviews/plotting/renderer.py:396, in Renderer.components(self, obj, fmt, comm, **kwargs)
    393 embed = (not (dynamic or streams or self.widget_mode == 'live') or config.embed)
    395 if embed or config.comms == 'default':
--> 396     return self._render_panel(plot, embed, comm)
    397 return self._render_ipywidget(plot)

File /usr/share/miniconda3/envs/test-environment/lib/python3.9/site-packages/holoviews/plotting/renderer.py:403, in Renderer._render_panel(self, plot, embed, comm)
    401 doc = Document()
    402 with config.set(embed=embed):
--> 403     model = plot.layout._render_model(doc, comm)
    404 if embed:
    405     return render_model(model, comm)

File /usr/share/miniconda3/envs/test-environment/lib/python3.9/site-packages/panel/viewable.py:738, in Viewable._render_model(self, doc, comm)
    736 if comm is None:
    737     comm = state._comm_manager.get_server_comm()
--> 738 model = self.get_root(doc, comm)
    740 if self._design and self._design.theme.bokeh_theme:
    741     doc.theme = self._design.theme.bokeh_theme

File /usr/share/miniconda3/envs/test-environment/lib/python3.9/site-packages/panel/layout/base.py:307, in Panel.get_root(self, doc, comm, preprocess)
    303 def get_root(
    304     self, doc: Optional[Document] = None, comm: Optional[Comm] = None,
    305     preprocess: bool = True
    306 ) -> Model:
--> 307     root = super().get_root(doc, comm, preprocess)
    308     # ALERT: Find a better way to handle this
    309     if hasattr(root, 'styles') and 'overflow-x' in root.styles:

File /usr/share/miniconda3/envs/test-environment/lib/python3.9/site-packages/panel/viewable.py:660, in Renderable.get_root(self, doc, comm, preprocess)
    658 wrapper = self._design._wrapper(self)
    659 if wrapper is self:
--> 660     root = self._get_model(doc, comm=comm)
    661     if preprocess:
    662         self._preprocess(root)

File /usr/share/miniconda3/envs/test-environment/lib/python3.9/site-packages/panel/layout/base.py:175, in Panel._get_model(self, doc, root, parent, comm)
    173 root = root or model
    174 self._models[root.ref['id']] = (model, parent)
--> 175 objects, _ = self._get_objects(model, [], doc, root, comm)
    176 props = self._get_properties(doc)
    177 props[self._property_mapping['objects']] = objects

File /usr/share/miniconda3/envs/test-environment/lib/python3.9/site-packages/panel/layout/base.py:157, in Panel._get_objects(self, model, old_objects, doc, root, comm)
    155 else:
    156     try:
--> 157         child = pane._get_model(doc, root, model, comm)
    158     except RerenderError as e:
    159         if e.layout is not None and e.layout is not self:

File /usr/share/miniconda3/envs/test-environment/lib/python3.9/site-packages/panel/pane/holoviews.py:453, in HoloViews._get_model(self, doc, root, parent, comm)
    451     del kwargs['height']
    452 child_pane = self._get_pane(backend, state, **kwargs)
--> 453 self._update_plot(plot, child_pane)
    454 model = child_pane._get_model(doc, root, parent, comm)
    455 if ref in self._plots:

File /usr/share/miniconda3/envs/test-environment/lib/python3.9/site-packages/panel/pane/holoviews.py:328, in HoloViews._update_plot(self, plot, pane)
    326             plot.update(key)
    327 else:
--> 328     plot.update(key)
    329     if hasattr(plot.renderer, 'get_plot_state'):
    330         pane.object = plot.renderer.get_plot_state(plot)

File /usr/share/miniconda3/envs/test-environment/lib/python3.9/site-packages/holoviews/plotting/mpl/plot.py:274, in MPLPlot.update(self, key)
    272 if len(self) == 1 and key in (0, self.keys[0]) and not self.drawn:
    273     return self.initialize_plot()
--> 274 return self.__getitem__(key)

File /usr/share/miniconda3/envs/test-environment/lib/python3.9/site-packages/holoviews/plotting/plot.py:443, in DimensionedPlot.__getitem__(self, frame)
    441 if not isinstance(frame, tuple):
    442     frame = self.keys[frame]
--> 443 self.update_frame(frame)
    444 return self.state

File /usr/share/miniconda3/envs/test-environment/lib/python3.9/site-packages/holoviews/plotting/mpl/plot.py:62, in mpl_rc_context.<locals>.wrapper(self, *args, **kwargs)
     60 def wrapper(self, *args, **kwargs):
     61     with _rc_context(self.fig_rcparams):
---> 62         return f(self, *args, **kwargs)

File /usr/share/miniconda3/envs/test-environment/lib/python3.9/site-packages/holoviews/plotting/mpl/element.py:1246, in OverlayPlot.update_frame(self, key, ranges, element)
   1243     self._create_dynamic_subplots(key, items, ranges)
   1245 # Update plot options
-> 1246 plot_opts = self.lookup_options(element, 'plot').options
   1247 inherited = self._traverse_options(element, 'plot',
   1248                                    self._propagate_options,
   1249                                    defaults=False)
   1250 plot_opts.update(**{k: v[0] for k, v in inherited.items()
   1251                     if k not in plot_opts})

File /usr/share/miniconda3/envs/test-environment/lib/python3.9/site-packages/holoviews/plotting/plot.py:288, in Plot.lookup_options(cls, obj, group)
    286 @classmethod
    287 def lookup_options(cls, obj, group):
--> 288     return lookup_options(obj, group, cls.backend)

File /usr/share/miniconda3/envs/test-environment/lib/python3.9/site-packages/holoviews/core/options.py:96, in lookup_options(obj, group, backend)
     93 except SkipRendering:
     94     style_opts = None
---> 96 node = Store.lookup_options(backend, obj, group)
     97 if group == 'style' and style_opts is not None:
     98     return node.filtered(style_opts)

File /usr/share/miniconda3/envs/test-environment/lib/python3.9/site-packages/holoviews/core/options.py:1277, in Store.lookup_options(cls, backend, obj, group, defaults)
   1274 @classmethod
   1275 def lookup_options(cls, backend, obj, group, defaults=True):
   1276     # Current custom_options dict may not have entry for obj.id
-> 1277     if obj.id in cls._custom_options[backend]:
   1278         return cls._custom_options[backend][obj.id].closest(
   1279             obj, group, defaults, backend=backend)
   1280     elif not defaults:

AttributeError: 'NoneType' object has no attribute 'id'

:DynamicMap   [realization,time]
   :Overlay
      .Image.I     :Image   [longitude,latitude]   (surface_temperature)
      .Coastline.I :Feature   [Longitude,Latitude]

Using dynamic mode means that the data for each frame is only extracted when you’re actually viewing that part of the data, which can have huge benefits in terms of speed and memory consumption. However, it relies on having a running Python process to render and serve each image, and so it cannot be used when generating static HTML output such as for the GeoViews web site.

Irregularly sampled data

Often gridded datasets are not regularly sampled, instead providing irregularly sampled multi-dimensional coordinates. Such datasets can be easily visualized using the QuadMesh element.

xrds = xr.tutorial.open_dataset('rasm').load()
qmesh = gv.Dataset(xrds.Tair).to(gv.QuadMesh, ['xc', 'yc'], dynamic=True).redim.range(Tair=(-30, 30))
qmesh.opts(colorbar=True, cmap='RdBu_r', projection=ccrs.Robinson()) * gf.coastline

Non-geographic views

So far we have focused entirely on geographic views of the data, plotting the data on a projection. However the conversion interface is completely general, allowing us to slice and dice the data in any way we like. For these views we will switch to the bokeh plotting extension:

hv.output(backend='bokeh')

The simplest example of this capability is simply a view showing the temperature over time for each realization, longitude, and latitude coordinate:

curves = dataset.to(hv.Curve, 'time', dynamic=True).overlay('realization')

curves.opts(
    opts.Curve(xrotation=25, width=600, height=400, framewise=True),
    opts.NdOverlay(legend_position='right', toolbar='right'))

WARNING:param.outer_fn: Callable raised "ValueError('None is not in list')".
Invoked as outer_fn(None, None)
WARNING:param.dynamic_operation: Callable raised "ValueError('None is not in list')".
Invoked as dynamic_operation(None, None)

Traceback (most recent call last):
  File "/usr/share/miniconda3/envs/test-environment/lib/python3.9/site-packages/holoviews/plotting/util.py", line 293, in get_plot_frame
    return map_obj[key]
  File "/usr/share/miniconda3/envs/test-environment/lib/python3.9/site-packages/holoviews/core/spaces.py", line 1216, in __getitem__
    val = self._execute_callback(*tuple_key)
  File "/usr/share/miniconda3/envs/test-environment/lib/python3.9/site-packages/holoviews/core/spaces.py", line 983, in _execute_callback
    retval = self.callback(*args, **kwargs)
  File "/usr/share/miniconda3/envs/test-environment/lib/python3.9/site-packages/holoviews/core/spaces.py", line 581, in __call__
    ret = self.callable(*args, **kwargs)
  File "/usr/share/miniconda3/envs/test-environment/lib/python3.9/site-packages/holoviews/util/__init__.py", line 1033, in dynamic_operation
    key, obj = resolve(key, kwargs)
  File "/usr/share/miniconda3/envs/test-environment/lib/python3.9/site-packages/holoviews/util/__init__.py", line 1022, in resolve
    return key, map_obj[key]
  File "/usr/share/miniconda3/envs/test-environment/lib/python3.9/site-packages/holoviews/core/spaces.py", line 1216, in __getitem__
    val = self._execute_callback(*tuple_key)
  File "/usr/share/miniconda3/envs/test-environment/lib/python3.9/site-packages/holoviews/core/spaces.py", line 983, in _execute_callback
    retval = self.callback(*args, **kwargs)
  File "/usr/share/miniconda3/envs/test-environment/lib/python3.9/site-packages/holoviews/core/spaces.py", line 581, in __call__
    ret = self.callable(*args, **kwargs)
  File "/usr/share/miniconda3/envs/test-environment/lib/python3.9/site-packages/holoviews/core/spaces.py", line 1559, in outer_fn
    selected = HoloMap(self.select(**dict(dim_vals)))
  File "/usr/share/miniconda3/envs/test-environment/lib/python3.9/site-packages/holoviews/core/spaces.py", line 1263, in select
    selection = super().select(selection_specs=selection_specs, **kwargs)
  File "/usr/share/miniconda3/envs/test-environment/lib/python3.9/site-packages/holoviews/core/dimension.py", line 1109, in select
    selection = self.get(tuple(select), None)
  File "/usr/share/miniconda3/envs/test-environment/lib/python3.9/site-packages/holoviews/core/ndmapping.py", line 541, in get
    return self[key]
  File "/usr/share/miniconda3/envs/test-environment/lib/python3.9/site-packages/holoviews/core/spaces.py", line 1205, in __getitem__
    cache = super().__getitem__(key)
  File "/usr/share/miniconda3/envs/test-environment/lib/python3.9/site-packages/holoviews/core/ndmapping.py", line 628, in __getitem__
    conditions = self._generate_conditions(map_slice)
  File "/usr/share/miniconda3/envs/test-environment/lib/python3.9/site-packages/holoviews/core/ndmapping.py", line 700, in _generate_conditions
    dim_slice = [dim.values.index(dim_val)
  File "/usr/share/miniconda3/envs/test-environment/lib/python3.9/site-packages/holoviews/core/ndmapping.py", line 700, in <listcomp>
    dim_slice = [dim.values.index(dim_val)
ValueError: None is not in list

Note that the longitude slider will have no effect, if latitude is -90 or +90, since there is only one data point for the North or South poles (regardless of the declared longitude). Here the .overlay gives a different curve for each realization; without it all realization values would be pooled together.

We can also make non-geographic 2D plots, for instance as a HeatMap over time and realization, at a specified longitude and latitude:

dataset.to(hv.HeatMap, ['realization', 'time'], dynamic=True).opts(width=600, colorbar=True, tools=['hover'])

Lower-dimensional views

So far all the conversions shown have incorporated each of the available coordinate dimensions explicitly. However, often times we want to see the spread of values along one or more dimensions, pooling all the other dimensions together. A simple example of this is a box plot where we might want to see the spread of surface_temperature on each day, pooled across all latitude and longitude coordinates. To pool across particular dimensions, we can explicitly declare the “map” dimensions, which are the key dimensions of the HoloMap container rather than those of the Elements contained in the HoloMap. By explicitly declaring no dimensions to groupby, we can tell the conversion interface to pool across all dimensions except the particular key dimension(s) supplied, in this case the 'time' (plot A) and 'realization' (plot B):

hv.output(backend='matplotlib')

hv.Layout([dataset.to(hv.Violin, d, groupby=[], datatype=['dataframe']).opts(xrotation=25)
           for d in ['time', 'realization']])

Reducing the data

So far all the examples we have seen have displayed all the data in some way or another. Another way to explore a dataset is to explicitly reduce the dimensionality or select subregions of a dataset. There are two main ways to do this—either we explicitly select a subset of the data, or we collapse a dimension using an aggregation function, e.g. by computing a mean along a particular dimension.

Selecting slices

Using the select method we can easily select ranges of coordinates in the dataset. Unfortunately, the select method does not currently know that latitude and longitude are cyclic, so instead we have to select regions at both ends of the prime meridian (0$^\circ$ longitude) and overlay them. In this way we can stitch together multiple cubes or xarrays or simply view specific subregions:

northern = dataset.select(latitude=slice(25, 75))
(northern.select(longitude=slice(260, 305)).to(gv.Image, geo_dims) *
 northern.select(longitude=slice(330, 362)).to(gv.Image, geo_dims) *
 gf.coastline)[::5, ::5]

Selecting a particular coordinate

To examine one particular coordinate, we can select it, cast the data to Curves, reindex the data to drop the now-constant latitude and longitude dimensions, and overlay the remaining ‘realization’ dimension:

hv.output(backend='bokeh')

curves = dataset.select(latitude=0, longitude=0).to(hv.Curve, ['time']).reindex().overlay()

curves.opts(width=600, height=400, legend_position='right', toolbar='above')

As you can see, with GeoViews and HoloViews it is very simple to select precisely which aspects of complex, multidimensional datasets that you want to focus on.

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